IDEAS home Printed from https://ideas.repec.org/p/zbw/fisisi/s102019.html
   My bibliography  Save this paper

A survey-based approach to estimate residential electricity consumption at municipal level in Germany

Author

Listed:
  • Huang, Charlotte
  • Elsland, Rainer

Abstract

In the context of the German Energiewende (energy transition), energy system modelling is used to investigate possible future scenarios of the national energy system. These models depend on regionally disaggregated input data to ade-quately capture interdependencies in the energy system at high resolution. In Germany, official energy consumption statistics are only published at the national (AGEB, 2019a) and federal state (LAK, 2019) levels - there are no official statis-tics on residential electricity consumption with higher regional resolution. So far, energy system modelling has typically relied on specific consumption val-ues or constant per-capita estimators (see Beer (2012) and Hartel et al. (2017)) to approximate residential electricity consumption with a regional resolution be-yond that of federal state. The use of primary data on electricity consumption at household level has so far been limited. Yet, primary data, e.g. from the German Residential Energy Consumption Survey (GRECS), displays heterogeneity in household-level electricity consumption, which cannot be captured by constant per-capita distribution keys. This study aims to investigate whether integrating primary data into quantitative modelling of energy systems contributes to a more realistic representation of regional electricity consumption by accounting for het-erogeneity in household electricity consumption. A synthetic population is generated via the Iterative Proportional Fitting (IPF) al-gorithm based on primary data on household electricity consumption taken from the German Residential Energy Consumption Survey (GRECS), as well as re-gion-specific data at municipal (Gemeindeebene) level taken from the 2011 German census. Total residential electricity consumption at municipal level is then inferred from the synthetic population. Estimates of total residential electricity consumption were derived for 2011 for all municipalities of the German state Rhineland-Palatinate and evaluated against benchmark values from the Net-zentwicklungsplan 2030 (Fraunhofer ISI, 2017) of the same year, which is avail-able at the regional resolution of German urban and rural districts, referred to here as "counties" (Stadt- und Landkreise). The derived estimates achieved an R² of around 0.99 with respect to benchmark values. Overall, the estimates were 6.8% below the benchmark value for Rhine-land-Palatinate. It can be concluded that Iterative Proportional Fitting (IPF) con-stitutes a viable approach to integrate primary data into deriving regional esti-mates of residential electricity consumption at municipal level.

Suggested Citation

  • Huang, Charlotte & Elsland, Rainer, 2019. "A survey-based approach to estimate residential electricity consumption at municipal level in Germany," Working Papers "Sustainability and Innovation" S10/2019, Fraunhofer Institute for Systems and Innovation Research (ISI).
    • Handle: RePEc:zbw:fisisi:s102019
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/209625/1/1685814336.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manuel Frondel and Gerhard Kussel, 2019. "Switching on Electricity Demand Response: Evidence for German Households," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    2. M Birkin & M Clarke, 1988. "Synthesis—A Synthetic Spatial Information System for Urban and Regional Analysis: Methods and Examples," Environment and Planning A, , vol. 20(12), pages 1645-1671, December.
    3. Floriana Gargiulo & Sônia Ternes & Sylvie Huet & Guillaume Deffuant, 2010. "An Iterative Approach for Generating Statistically Realistic Populations of Households," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    4. Cathal O'Donoghue & Karyn Morrissey & John Lennon, 2014. "Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 26-75.
    5. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    6. Stanislav Kolenikov, 2014. "Calibrating survey data using iterative proportional fitting (raking)," Stata Journal, StataCorp LP, vol. 14(1), pages 22-59, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Trudeau, Jennifer M. & Alicea-Planas, Jessica & Vásquez, William F., 2020. "The value of COVID-19 tests in Latin America," Economics & Human Biology, Elsevier, vol. 39(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    2. Ricardo Crespo & Ignacio Hernandez, 2020. "On the spatially explicit Gini coefficient: the case study of Chile—a high-income developing country," Letters in Spatial and Resource Sciences, Springer, vol. 13(1), pages 37-47, April.
    3. Ian Philips & Graham Clarke & David Watling, 2017. "A Fine Grained Hybrid Spatial Microsimulation Technique for Generating Detailed Synthetic Individuals from Multiple Data Sources: An Application To Walking And Cycling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 167-200.
    4. Damianakis, Nikolaos & Mouli, Gautham Ram Chandra & Bauer, Pavol & Yu, Yunhe, 2023. "Assessing the grid impact of Electric Vehicles, Heat Pumps & PV generation in Dutch LV distribution grids," Applied Energy, Elsevier, vol. 352(C).
    5. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    6. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
    7. John Curtis & Brian Stanley, 2016. "Analysing Residential Energy Demand: An Error Correction Demand System Approach for Ireland," The Economic and Social Review, Economic and Social Studies, vol. 47(2), pages 185-211.
    8. Omar Shafqat & Elena Malakhtka & Nina Chrobot & Per Lundqvist, 2021. "End Use Energy Services Framework Co-Creation with Multiple Stakeholders—A Living Lab-Based Case Study," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    9. Wei Yu & Baizhan Li & Yarong Lei & Meng Liu, 2011. "Analysis of a Residential Building Energy Consumption Demand Model," Energies, MDPI, vol. 4(3), pages 1-13, March.
    10. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    11. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    12. Martin Johnsen & Oliver Brandt & Sergio Garrido & Francisco C. Pereira, 2020. "Population synthesis for urban resident modeling using deep generative models," Papers 2011.06851, arXiv.org.
    13. Raúl Arango-Miranda & Robert Hausler & Rabindranarth Romero-López & Mathias Glaus & Sara Patricia Ibarra-Zavaleta, 2018. "An Overview of Energy and Exergy Analysis to the Industrial Sector, a Contribution to Sustainability," Sustainability, MDPI, vol. 10(1), pages 1-19, January.
    14. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    15. Estiri, Hossein, 2014. "Building and household X-factors and energy consumption at the residential sector," Energy Economics, Elsevier, vol. 43(C), pages 178-184.
    16. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    17. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    18. Nouha Dkhili & David Salas & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2021. "Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation," Energies, MDPI, vol. 14(6), pages 1-28, March.
    19. Muratori, Matteo & Roberts, Matthew C. & Sioshansi, Ramteen & Marano, Vincenzo & Rizzoni, Giorgio, 2013. "A highly resolved modeling technique to simulate residential power demand," Applied Energy, Elsevier, vol. 107(C), pages 465-473.
    20. Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2015. "Long term outlook of primary energy consumption of the Italian thermoelectric sector: Impact of fuel and carbon prices," Energy, Elsevier, vol. 87(C), pages 153-164.
    21. Xavier Faure & Tim Johansson & Oleksii Pasichnyi, 2022. "The Impact of Detail, Shadowing and Thermal Zoning Levels on Urban Building Energy Modelling (UBEM) on a District Scale," Energies, MDPI, vol. 15(4), pages 1-18, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:fisisi:s102019. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/isfhgde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.